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Inflation, Forecast Intervals and Long Memory Regression Models

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Author Info
Charles S. Bos () (Erasmus University Rotterdam)
Philip Hans Franses () (Erasmus University Rotterdam)
Marius Ooms () (Vrije Universiteit Amsterdam)

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Abstract

We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years. Even after correcting for the effect of explanatory variables, there is conclusive evidence of both fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s and we incorporate these breaks in the forecasting model for the 1980s and 1990s. We compare the results of the fractionally integrated ARFIMA(0,d,0) model with those for ARIMA(1,d,1) models with fixed order of d=0 and d=1 for inflation. Comparing mean squared forecast errors, we find that the ARMA(1,1) model performs worse than the other models over our evaluation period 1984-1999. The ARIMA(1,1,1) model provides the best forecasts, but its multi-step forecast intervals are too large.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-029/4.

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Date of creation: 14 Mar 2001
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Handle: RePEc:dgr:uvatin:20010029

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188. [Downloadable!] (restricted)
  2. Ooms, Marius & Hassler, Uwe, 1997. "On the effect of seasonal adjustment on the log-periodogram regression," Economics Letters, Elsevier, vol. 56(2), pages 135-141, October. [Downloadable!] (restricted)
  3. Jordi Gali & Mark Gertler, 2000. "Inflation Dynamics: A Structural Econometric Analysis," NBER Working Papers 7551, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  4. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  5. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449. [Downloadable!] (restricted)
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  6. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
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  7. David Harvey & Paul Newbold, 2000. "Tests for multiple forecast encompassing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 471-482. [Downloadable!]
  8. Ball, Laurence & Mankiw, N Gregory, 1995. "Relative-Price Changes as Aggregate Supply Shocks," The Quarterly Journal of Economics, MIT Press, vol. 110(1), pages 161-93, February. [Downloadable!] (restricted)
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  9. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
  10. West, Kenneth D, 2001. "Tests for Forecast Encompassing When Forecasts Depend on Estimated Regression Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 29-33, January.
  11. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  12. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
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  1. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada. [Downloadable!]
  2. Markku Lanne, 2006. "A Mixture Multiplicative Error Model for Realized Volatility," Economics Working Papers ECO2006/3, European University Institute. [Downloadable!]
    Other versions:
  3. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  4. Chien-Chiang Lee & Chun-Ping Chang, 2007. "Mean reversion of inflation rates in 19 OECD countries: Evidence from panel Lm unit root tests with structural breaks," Economics Bulletin, Economics Bulletin, vol. 3(23), pages 1-15. [Downloadable!]
  5. N. Hyung & P.H.B.F. Franses, 2001. "Structural breaks and long memory in US inflation rates," Econometric Institute Report 221, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  6. Claudio Morana, 2002. "Common Persistent Factors in Inflation and Excess Nominal Money Growth and a New Measure of Core Inflation," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 6(3), pages 1092-1092. [Downloadable!] (restricted)
  7. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics. [Downloadable!]
    Other versions:
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